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Mar 8, 2021 - Python
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federated-learning
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A library for answering questions using data you cannot see
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A curated list of references for MLOps
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Feb 24, 2021
An Industrial Grade Federated Learning Framework
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Mar 8, 2021 - Python
Federated Learning Library: https://fedml.ai
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A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561
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Dec 14, 2020 - Python
Awesome Multitask Learning Resources
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resources about federated learning and privacy in machine learning
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Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
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Sep 30, 2020 - Python
danieljanes
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Feb 4, 2021
A curated list of awesome Distributed Deep Learning resources.
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Jul 19, 2019
算法刷题指南、Java多线程与高并发、Java集合源码、Spring boot、Spring Cloud等笔记,源码级学习笔记后续也会更新。
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Feb 1, 2021 - HTML
Infrastructures™ for Machine Learning Training/Inference in Production.
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Simulate a federated setting and run differentially private federated learning.
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Manage federated learning workload using cloud native technologies.
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A privacy preserving NLP framework
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Feb 20, 2021 - Python
All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
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Jan 28, 2021 - Shell
A curated list of awesome edge machine learning resources, including research papers, inference engines, challenges, books, meetups and others.
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Apr 18, 2020 - Python
See new version https://github.com/mccorby/PhotoLabeller
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Apr 26, 2018 - Java
Federated Learning: Client application doing classification of images and local training. Works better with the Parameter Server at https://github.com/mccorby/PhotoLabellerServer
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Fair Resource Allocation in Federated Learning (ICLR '20)
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Xaynet represents an agnostic Federated Machine Learning framework to build privacy-preserving AI applications.
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Galaxy Federated Learning Framework (星际联邦学习框架)
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Bayesian Nonparametric Federated Learning of Neural Networks
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A scalable, high-performance serving system for federated learning models
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An open framework for Federated Learning.
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ivelin
commented
Dec 13, 2020
Is your feature request related to a problem? Please describe.
Currently ambianic-edge is packaged with its source directly in a docker container.
Describe the solution you'd like
For better modularity and portability, we should package as a python wheel as we already do with peerjs-python.
Additional context
There will be some tricky parts with declaring optional or on-deman
Personalized Federated Learning using Pytorch (pFedMe)
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Jan 31, 2021 - Python
This repo contains all the notebooks mentioned in blog.
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Jan 26, 2020 - Jupyter Notebook
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It seems that the number of joining clients (not the num of computing clients) is fixed in fedml_api/data_preprocessing/**/data_loader and cannot be changed except CIFAR10 datasets.
Here I mean that it seems the total clients is decided by the datasets, rather the input from run_fedavg_distributed_pytorch.sh.
https://github.com/FedML-AI/FedML/blob/3d9fda8d149c95f25ec4898e31df76f035a33b5d/fed